Month: May 2017

Framework for valuing fixed income – Long end

I do a very different analysis of the long-end of the yield curve, compared to the front-end. (Framework for valuing fixed income – Front end) Mathematically, you could take the same approach and bootstrap the curve from a complete set of forecasts of short-term rates for the next 30 years. But this seems a bit silly and begs the question of how you would get these forecasts anyway.
To simplify the analysis, what we have to work out is what the long-term “equilibrium” rate will be and ignore for now how we get there or use the analysis from the front end to build a path.

Simple Hypothesis: Long-Term rates = Nominal GDP

An approach that appeals to me is to look for a link between long term interest rates and long term nominal GDP. I think of it as a “Wicksellian” natural rate which the market will tend to revert to i.e. If interest rates are consistently far away from the growth rate of nominal GDP then there would be a persistent drag or stimulus to growth which would not be sustainable. You can get to a similar idea from several different economic frameworks.

If we look at the data then, the hypothesis looks reasonable. Below is the 10-year average of nominal GDP growth alongside the 10y10y interest rate for the US. The 10y10y rate is the rate you can calculate as what the market implies the 10y interest rate to be in 10 years’ time.

Before the early 2000s, interest rates were consistently a little higher than GDP. Academics were happy with this and explained it in terms of some type of premium which bond owners would demand to own bonds. They were then confused in the early 2000s by the “conundrum” that long term yields dipped, explaining it either by Chinese ownership of Treasuries or a global “savings glut” which was forcing down yields.

Outlook for Nominal GDP

Current yields do not look very remarkable to me, but they are only correct if you think that nominal GDP will remain as low as for the past decade. The most prominent argument that we should expect this to continue comes from Larry Summers and his promotion of the idea of “Secular Stagnation” – http://larrysummers.com/2016/02/17/the-age-of-secular-stagnation/

I find these arguments a little hard to engage with as we must recognise how utterly useless long-term forecasts of anything generally are. I should admit that I am not a big fan of anything which looks like a restatement of the savings glut theory to me, but I do not want to engage here in an academic debate. As a more practical question, I think that the burden of proof is on ideas such as Secular Stagnation and the “New Normal” that the world will need permanently far lower rates than it has in the past. Arguing that nominal GDP will be lower, due to slower population growth, demographics and potentially lower productivity is easy. Explaining why it is 3% lower is not so easy.

My view is that this economic cycle does not require new theories to explain it. A financial crisis results in a very deep recession and leaves scars which mean the recovery is slower than many expect. These hangovers from the financial crisis are what Yellen refers to as “headwinds” which are slowing down the economy. Risk aversion among consumers and businesses after such a bad recession is only to be expected and the impairment of the credit channel after such a disruption is also understandable. But there is no reason to think that these headwinds are permanent. They can abate and we can return to a world similar to the one before, both in terms of the level of nominal GDP and also the relationship between interest rates and growth. The financial crisis has been traumatic, especially for countries like the US and the UK, that have not seen one like this recently. However, the history of financial crises is that they are worse than people think, but they are not permanent.

Are we renormalizing?

Unemployment fell slowly but is now down to 4.5%. wages have been sluggish but are now picking up.

If I draw the first chart again but this time use a 5yr rather than 10yr moving average then perhaps I can argue the market is reacting too slowly. Nominal GDP has been rising recently and with rising wages and inflation can easily be seen to be likely to continue to do so. If that is true then market rates are too low.

Why are long term rates still so low?

The idea that long term rates are too low is hardly new. After all this was the whole point of QE!! The central banks buy huge amounts of long term debt to drive up bond prices and yields down. This helps to stimulate the economy and boost other asset classes which look relatively cheaper to bond markets, and so drives reallocation flows.

As I mentioned in this post (https://appliedmacro.com/2017/05/01/government-debt-framework-uk-follow-up/), we are living in a new era of financial repression. Therefore, I really do not need any grand theory from the supply side of the economy to explain low rates. I just look at the huge boost in demand for bonds from the central banks.

Is there a catalyst for change?

  1. One potential catalyst would be from the front end. If the Fed hikes rates faster than the market expects, then this can cause a shock to ripple down the whole curve. We saw an extreme version of this in 1994.
  2. If wages start to accelerate then the Fed, economists and market participants would have to radically reassess their assumptions about the inflation outlook and the appropriate level of rates. If you are very confident this cannot happen, you have more faith in our understanding of this type of macro variable than I have.
  3. Even without any fundamental driver we may see a repricing simply from a change in the supply and demand dynamics of the bond market.

QE buying has been high for the past few years but it is finally slowing down. This may be the catalyst for a repricing of bonds.

Conclusion

A simple and yet historically useful framework for considering long term rates is to use nominal GDP. In recent years, we have seen the combination of a major downshift in long term expectations for both nominal GDP and the level of rates relative to nominal GDP. While many arguments justifying this change as permanent have some merit, I think that they are more temporary then current market pricing implies. Which means that I do not think that bond markets are cheap. In fact, I think they are wildly expensive.

 

Is Climate Science True?

If this blog were the BBC, in an effort for impartiality, I would give equal time to 2 ideas:

  1. Climate change is completely true
    Agreed by all reputable scientists and means drastic action needs to be taken immediately
  2. Climate change is a hoax
    Devised by the Chinese to limit the US economy and we need do nothing

However, I’m not bound by any such desire for manufactured impartiality so would like to ignore climate denial theories. I want effective action to be taken and I’m interested in why the first argument is failing to find broad enough support against those who want to dismantle the Paris Agreement.

The Science

I think I can reasonably simplify the scientific arguments to this:

  1. There is an empirical relationship between carbon and temperature
  2. There is a causal theory relating the two, the greenhouse effect
  3. There is no better theory, in fact no other credible theory

The criticisms for each item can be summarised like this:

  1. Poor and incomplete data
  2. The underlying system is dynamic and complex
  3. The models are not very precise with large amounts of uncertainty.

The responses I have seen to these criticism from climate change scientists are:

  1. We are collecting more data all the time. We are cleaning up the old data sets.
  2. We are building more complex models which incorporate more variables
  3. We are working to improve our accuracy with better models to reduce the level of uncertainty.

Coming from a macroeconomics background, the criticisms wouldn’t bother me much. We face them all the time. The responses from the climate change scientists do bother me however. If this reflects their research programmes, I fear they are heading in the wrong direction, sharing many of the methodological problems we see in macro and likely making the same mistakes.

My view

My preferred responses to the criticism would be

  1. Yes, the data is poor and incomplete. Not much we can do about that. We will not clean up data to pretend it is better than it is.
  2. Yes, it is dynamic and complex. Attempting to making models more complex would mean data-mining the limited data sets to produce models which are pretty but have no validity. Simple models of complex systems often work much better. Read the Borges short story (https://appliedmacro.com/2017/05/11/models/) for an elegant refutation of the idea that seeking perfection in models leads to good outcomes.
  3. Accurate forecasts are not possible because we do not have ‘out of sample’ data and associated feedback. There is little new data to use so we cannot properly test hypotheses in the way we can with weather forecasting.

The good news is that we do not need accurate forecasts because:

  1. If our model provides an unbiased best estimate and uncertainty is two-sided, then the level of uncertainly around it does not affect the policy response. Essentially the policy response will be versus the expected increase in temperature.
  1. We are providing conditional, not unconditional forecasts.
    To take an analogy, I am thinking of running the London marathon next year. Please estimate how long it will take me to run it i) in running kit ii) wearing a gorilla costume. I would strongly expect that your confidence in both of your answers is very low. However, I bet you are very confident that ii) will take longer than i).

Financial markets can be a very good training ground for learning about practical model building and their methodical dangers. Most of us have been seduced by beautiful models with great back tests with desirable correlations and high Sharpe ratios. But then trying to use them to make money, we find they had no predictive qualities at all. After enough painful experiences, we learn to be highly suspicious of any model that fits the data too well – it is the obvious symptom of data-mining. The models that work in practice are the ones that are intuitive, simple and accept that the world is a messy complicated place.

What climate change scientists and macro-economists can actually do have a lot of similarities. We do not know what the temperature will be on October 23rd 2087 but we have a good guess it will be higher if there is more carbon in the atmosphere.

Framework for valuing fixed income – Front end

In a previous post, we looked at a model of relative value of equities versus bonds (https://appliedmacro.com/2017/05/09/are-equities-expensive-part-i/).
But it does beg the question of whether bonds are good value themselves.

I am not aiming for a full review of global bond value, I will focus purely on the US market. In this post, I shall look at the front end of the curve and in a later post the long end.

Expectations

The simplest and best model for the short end of the yield curve is the expectations hypothesis.
The yield is an average of short-term interest rates that are expected to prevail through the life of the security


Such expectations may not match the market yield, so there may be a residual. This residual r is sometimes called the premium (choose any: risk premium, term premium, liquidity premium, it does not matter which). At times such as during the financial crisis, I spent a long time modelling precisely the premia, but in normal market conditions it’s not very productive. Merely knowing if the premium is large or small, positive or negative is sufficient.

The other term often used for premium is expected return. If you think in terms of academic “efficient market” models or asset allocation in a real money environment, then you may prefer to use excess return but the language does not matter here.

US Front End

In the US, the Federal Reserve effectively sets short term interest rates, the Fed Funds rate, and these days they helpfully publish quarterly forecasts of where the committee thinks it will be. A sensible starting point is to compare these forecasts to the tradable yield and calculate the residual.

 

If you have not been following fixed income markets for the last few years or have learnt how markets work from finance textbooks, you may find this chart surprising.
We, as market participants, are well used to the fact that the market is pricing that rates will be significantly lower than the people who set them expect them to be. This has been the case for a long time but so far, the market has been better at predicting how the Fed will behave than the Fed itself.

If we look at a chart over the past 2 years where rates have been expected to be at the end of 2018, we see some fluctuations but very little net movement. In contrast, the Fed has been consistently revising lower its forecasts of where it thinks rates will be.

If we cannot just assume the Fed know what they will do, we must form our own opinion on where rates might go and determine whether the market is under or over pricing the path. The way to do this is to break down the elements of the forecast and analyse each of them.

The Fed’s reaction function & the Taylor rule

To start with the obvious, the Fed decision can be thought of as a function of things they care about. It is often called their “reaction function” and the things they care about are employment and inflation, their explicit objectives as given to them by Congress.

A common and useful form of this is the Taylor rule, which models Fed behaviour on just two variables.


Using this to make investments

The Taylor Rule is not that useful as a predictor of rates, but it forms a useful framework to think of what drives them.

There are 3 obvious places where you can disagree with the market and so make an investment call.

  1. A different view on growth

One of the largest and most obvious trades in my career was short term rates in 2002. The economy had been very poor in 2001, but the memory of the bubble was perhaps still so vivid that the market priced a rapid rebound in growth and thus interest rates. 2002 did not turn out to be the year of recovery and rate expectations fell accordingly all year.

  1. A different model of the economy

A good example of this would be 2008. Even after Lehman went under in October 2008, it took a long time for people to understand how serious it was and the devastating impact on the broader economy. The market was still pricing that rates would be nearly 3% at the end of 2009. They ended up close to zero. Rates eventually plummet in 2008 because the economy is falling apart.

A counter-example where a commonly believed idea turns out to be wrong is the idea that Quantitative Easing (QE) is going to lead to high inflation and so bonds will collapse. This comes from the idea that inflation is caused by “money” and the Fed is “printing money”. A simple and appealing argument that comes from a misunderstanding of what “money” is and how the monetary and banking system works. (a good topic and controversial later post I am sure).

  1. A different view on reaction function.

An example here would be that after the crisis many people were very premature in thinking that the economy would get back to normal.

In the summer of 2013, rates were still zero and the Taylor Rule suggested that was appropriate. But taking the economic forecasts at the time and projecting what that meant, suggested that rates would be much higher. So back in 2013 the market was pricing that rates would currently be about 3 %. In fact they are around 1%.

This difference is not because the economic growth forecasts were wrong. But the reaction function was. If you listened to Fed Chair Yellen’s speeches she was clear that the Fed would be very “patient” in raising rates. They desperately wanted to avoid hiking prematurely and actually wanted inflation to be higher. So a new reaction function should have been understood – that the Fed were waiting longer to hike to get the economy to be running hotter.

What about now?

My experience of financial markets is that is that expectations are more commonly adaptive than rational. By this I mean that humans (including market participants) tend to overweight recent experience. Given that the Fed has been consistently too high in their forecasts for the last few years, people expect that will continue to be the case. I am not so sure.

I am inclined to use an even simpler new reaction function for the Fed based upon wages. In previous cycles, they would hike before wages rose because

  1. They were confident in the economy
  2. Inflation and wages were high enough already to take a risk if they go lower again
  3. Wages are a lagging indicator, so by the time wages rise the economy will have been running too hot for too long

This time they want wages and inflation to be higher before they even start. The data suggests to me that wage growth is finally recovering.

It is reasonable to think that the economic cycle works the same now as in previous periods, and so wages are a lagging indicator. That means that the labour market has been tight for a while now and is continuing to get getting tighter adding more upward pressure on wages.

Conclusion

This cycle has been very different from prior periods because

  1. The recession was very deep
  2. The recovery was slow
  3. The Fed wanted to wait until they were sure they needed to raise rates.

This has meant that being long the front end has been a reasonable trade for a long time i.e. the front end was cheap against my expectation of where the Fed would set rates. But with the signal that wages are finally rising, we may be approaching the end of this phase. Furthermore, with so little still priced for rate hikes from the Fed the front end does not look good value to me.

If the US recovery has been slow, but the economy not long-term impaired then this means that the rate cycle has been delayed, not that it is not coming or that where rates end up will be so much lower than in previous cycles. But that is the topic for the next post.